Quantitative Traits
Preliminaries
If you are already familiar with the structure of these exercises, read the Introduction first.
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Contact information
If you have questions about these exercises, please contact Dr. Kevin Middleton (middletonk@missouri.edu) or drop by Tucker 224.
Learning Objectives
The learning objectives for this exercise are:
- Explain how polygenic traits differ from Mendelian traits
- Explain how traits with continuous (also called quantitative) phenotypic measures result from the combined effects of many different genes
- Describe how many genes can each contribute a small amount to a phenotype
- Explain what quantitative trait loci (QTL) are and how QTL are discovered
- Explain how the contributions of many genes of small effect can be associated with a disease or condition
Contrasting Mendelian traits and polygenic traits
Dominant/recessive to just thinking about alternate alleles (major vs. minor)
What are quantitative traits?
Counting the ways: Binomial Coefficent
Continuous traits from combinations of many Mendelian traits
Combinations of alleles are binomial
Large numbers of small additions and subtractions are normal
Make some assumptions:
- Additivity can mean adding negative numbers
- All genes have roughly equal effect
- Gene do not interact with one another
Case study: Human height
- Best understood quantitative trait
- Yet still 700 genes
Observed variation is fixed. So adding more traits just means that each can explain a bit less of the variation while simultaneously explaining more of the variation.
NH <- readRDS("NHANES/NHANES.Rds")The National Health and Nutrition Examination Survey (“NHANES”) began in the early 1960’s and continues to this day. The goal is to assess the health and nutrition status of a broad cross-section of the population. As part of this study, height (in cm) and body mass (in kg) are recorded for each participant.
Data from the 2017-2020 NHANES survey has data for 13137
NH |>
group_by(Sex) |>
summarize(across(.cols = everything(), list(mean = mean, sd = sd)))# A tibble: 2 × 7
Sex Age_mean Age_sd Weight_mean Weight_sd Height_mean Height_sd
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 XX Female 36.1 24.0 66.1 29.3 152. 19.7
2 XY Male 35.9 24.5 72.6 32.1 161. 24.3
ggplot(NH, aes(Height, fill = Sex)) +
geom_histogram(bins = 30, show.legend = FALSE) +
scale_fill_manual(values = c("goldenrod", "firebrick")) +
facet_grid(Sex ~ .) +
labs(x = "Height (cm)", y = "Count")Case study: QTL mapping
Shapiro pigeon example (dominant trait)
Threshold traits
Schizophrenia (~200 genes)
Why family history is one of the most important diagnostic tools in medicine